CTFR 18/883,782 CTFR 97728 DETAILED ACTION The present office action represents a final action on the merits. Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Priority This application claims the priority date of provisional application 63/583,224 of September 15, 2023. 12-151 AIA 26-51 12-51 Status of Claims Claims 1, 8, and 15 are amended and claims 1-20 are pending. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1-7 are drawn to a computer-implemented method, which is within the four statutory categories (i.e., process). Claims 8-14 are drawn to a system, which is within the four statutory categories (i.e., machine). Claims 15-20 are drawn to one or more non-transitory computer-readable media storing instructions which, when executed by one or more processors, cause a system to perform operations, which is within the four statutory categories (i.e., machine). Claims 1-7 recite a computer-implemented method comprising : accessing a plurality of first prompts, wherein each first prompt of the plurality of first prompts is a prompt for generating a portion of a Subjective, Objective, Assessment and Plan (SOAP) note using a machine-learning model ; for each first prompt of the plurality of first prompts: (i) using a respective first prompt to obtain a first result from a first machine-learning model, (ii) providing, by an automated prompt manager, the respective first prompt and the first result to a second-machine-learning model to obtain a second result that includes an assessment of the first result, (iii) providing, by the automated prompt manager, the second result to a third machine-learning model to obtain a third result from that includes a second prompt, (iv) setting, by the automated prompt manager, the second prompt as the respective first prompt, (v) repeating steps (i)-(iv) a predetermined number of times to obtain a production prompt, (vi) adding the production prompt to a collection of prompts; and storing the collection of prompts in a non-transitory storage medium . Claims 8-14 recite in addition to the same abstract idea that is recited in claim 1: a system comprising : one or more processing systems ; and one or more computer-readable media storing instructions which, when executed by the one or more processing systems, cause the system to perform operations comprising : accessing a plurality of first prompts, wherein each first prompt of the plurality of first prompts is a prompt for generating a portion of a Subjective, Objective, Assessment and Plan (SOAP) note using a machine-learning model; for each first prompt of the plurality of first prompts: (i) using a respective first prompt to obtain a first result from a first machine-learning model, (ii) providing, by an automated prompt manager, the respective first prompt and the first result to a second-machine-learning model to obtain a second result that includes an assessment of the first result, (iii) providing, by the automated prompt manager, to a third machine- learning model to obtain a third result from that includes a second prompt, (iv) setting, by the automated prompt manager, the second prompt as the respective first prompt, (v) repeating steps (i)-(iv) a predetermined number of times to obtain a production prompt,(vi) adding the production prompt to a collection of prompts; and storing the collection of prompts in a non-transitory storage medium . Claims 15-20 recite in addition to the same abstract idea that is recited in claim 1: one or more non-transitory computer-readable media storing instructions which, when executed by one or more processors, cause a system to perform operations comprising . The bolded limitations, given the broadest reasonable interpretation, cover a certain method of organizing human activity and mathematical concepts, but for the recitation of generic computer components (e.g., in this case a system, one or more processing systems.). The underlined limitations are not part of the identified abstract idea (the method of organizing human activity or mathematical concepts) and are deemed “additional elements,” and will be discussed in further detail below. Dependent claims 2-7, 9-14, and 16-20 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. These limitations only serve to further limit the abstract idea (or contain the same additional elements found in the independent claim), and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 8, and 15 . The dependent claims recite additional limitations but these only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 8, and 15 . The additional elements from claims 1, 8, and 15 include: storing the collection of prompts in a non-transitory storage medium (apply it, MPEP 2106.05(f)). The additional elements from claim 8 include: a system comprising (apply it, MPEP 2106.05(f)). one or more processing systems (apply it, MPEP 2106.05(f)). one or more computer-readable media storing instructions which, when executed by the one or more processing systems, cause the system to perform operations comprising (apply it, MPEP 2106.05(f)). The additional elements from claim 15 include: one or more non-transitory computer-readable media storing instructions which, when executed by one or more processors, cause a system to perform operations comprising (apply it, MPEP 2106.05(f)). The dependent claims recite the same abstract idea that is recited in claims 1. These additional elements, in the independent claims are not integrated into a practical application because the additional elements (i.e., the limitations not identified as part of the abstract idea) amount to no more than limitations which: amount to mere instructions to apply an exception – for example, the recitation of a storage medium, a system, one or more processing systems, one or more computer- readable media storing instructions which, when executed by the one or more processing systems, one or more non-transitory computer-readable media storing instructions which, when executed by one or more processors, See Specification paragraphs [0040], [0051], [0061, [0083], and [0194] (See MPEP 2106.05(f)). Furthermore, the claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because, the additional elements (i.e., the elements other than the abstract idea) amount to no more than limitations which: amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by: The Specification discloses that the additional elements are well-understood, routine, and conventional in nature (i.e., the Specification, paragraphs [0040], [0051], [0061, [0083], and [0194] discloses that the additional elements (i.e., a storage medium, a system, one or more processing systems, one or more computer-readable media storing instructions which, when executed by the one or more processing systems, one or more non-transitory computer-readable media storing instructions which, when executed by one or more processors) comprise a plurality of different types of generic computing systems that are configured to perform generic computer functions (i.e., receiving and transmitting data) that are well understood routine, and conventional activities previously known to the pertinent industry (i.e., healthcare.); Dependent claims 2-7, 9-14, and 16-20 include other limitations, but none of these functions are deemed significantly more than the abstract idea. Thus, taken alone, the additional elements do not amount to “significantly more” than the above identified abstract idea. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves any other technology, and their collective functions merely provide conventional computer implementation. The application, is an attempt to organize human activity or mathematical concepts, automatic prompt engineering using a large language model. The inventive concept is the automatic prompt engineering using a large language model, which is not patentable. Therefore, whether taken individually or as an ordered combination, claims 1-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-21-aia AIA Claim s 1-2, 4, 8-9, 11, 15-16, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Lipton (U.S. Pub. No. 2022/0375605 A1) in view of Chintagunta (U.S. Pub. No. 2024/0029714 A1) and Anthony (U.S. Pub. No. 2025/0004450 A1) . Regarding claim 1 , Lipton discloses computer-implemented method comprising: accessing a plurality of labels, wherein each first label of the plurality of first label is a label for generating a portion of a Subjective, Objective, Assessment and Plan (SOAP) note using a machine-learning model (Paragraphs [0038]-[0042], [0044], FIG. 9 discuss machine learning optimized SOAP notes based on the human generated transcripts, uses the historical data to iteratively train the machine learning model, the data processing system extracts the noteworthy utterances using multi-label classification and assigns the extracted noteworthy utterances to summary section(s).); adding the production label to a collection of labels (Paragraph [0042], and FIGS 2, 3, and 9 discuss generates useful structure in the form of additional annotations that (i) segment each note into 15 subsections (not every subsection features in every note; and (ii) identify, for each sentence in the note, a set of corresponding supporting utterances in the conversation and generating the SOAP note.); and storing the collection of labels in a non-transitory storage medium (Paragraphs [0017] and [0032], Claim 1 discuss one or more non-transitory computer-readable media storing instructions and memory storing data and data store for storage.). Lipton does not explicitly disclose: first prompts; for each first prompt of the plurality of first prompts: (i) using a respective first prompt to obtain a first result from a first machine-learning model, (ii) providing, by an automated prompt manager, the respective first prompt and the first result to a second-machine-learning model to obtain a second result that includes an assessment of the first result, (iii) providing, by the automated prompt manager, the second result to a third machine-learning model to obtain a third result from that includes a second prompt, (iv) setting, by the automated prompt manager, the second prompt as the respective first prompt, (v) repeating steps (i)-(iv) a predetermined number of times to obtain a production prompt, and prompt. Chintagunta teaches: for each first prompt of the plurality of first prompts (Paragraphs [0008], FIG. 4 discuss obtain sentences related to for example, “Summarization” after a first round of training.): (i)using a respective first prompt to obtain a first result from a first machine-learning model (Examiner notes that the prior art does not explicitly state “first”, “second”, or “third” however different models are used.) (Paragraph [0029] discusses first, the component use heuristics to generate turn-level pseudolabels and train a transformer-based model, which is then applied on sentences to create noisy sentence-level labels.), (ii) providing, by an automated prompt manager, the respective first prompt and the first result to a second-machine-learning model to obtain a second result that includes an assessment of the first result (Paragraphs [0029]-[0030], [0047], and [0225]-[0230] discuss using the transformer-based model, iteratively refines sentence-level labels using a cluster-based human-in-the-loop approach; use a generative machine learning model, e.g., GPT-3, as the backbone of the algorithm and scale human labeled examples to yield results comparable to using 6400 human labeled examples (˜30×) leveraging low-shot learning and an ensemble method. The component summarizes medical conversation summarization by discretizing the task into several smaller dialogue understanding tasks that are sequentially built upon. The component identifies medical entities and their affirmations within the conversation to serve as building blocks. The component then dynamically constructs few-shot prompts for tasks by conditioning on relevant patient information and use a generative machine learning model (e.g., GPT-3) as the backbone.), (iii) providing, by the automated prompt manager, the second result to a third machine-learning model to obtain a third result from that includes a second prompt (Paragraphs [0029]-[0030], [0047], [0075], [0077]-[0079], [0171], FIG. 11, Table 9 discuss using various models, medical dialogue summarization models, using the transformer-based model, iteratively refines sentence-level labels using a cluster-based human-in-the-loop approach. Each iteration requires only a few dozen annotator decisions; dynamically constructs few-shot prompts for tasks by conditioning on relevant patient information and use a generative machine learning model (e.g., GPT-3) as the backbone.), (v)repeating steps (i)-(iv) a predetermined number of times to obtain a production prompt (Paragraph [0113] discusses stop training when the objective has not improved over more than 5 consecutive validation runs.), prompts (Paragraph [0030] discusses dynamically construct prompts for tasks by conditioning on relevant patient information and use a generative machine learning model.). Therefore, it would have been obvious to one of ordinary skill in the art to modify Lipton to include, for each first prompt of the plurality of first prompts, (i)using a respective first prompt to obtain a first result from a first machine-learning model, (ii) providing, by an automated prompt manager, the respective first prompt and the first result to a second-machine-learning model to obtain a second result that includes an assessment of the first result, (iii) providing, by the automated prompt manager, the second result to a third machine-learning model to obtain a third result from that includes a second prompt, (v)repeating steps (i)-(iv) a predetermined number of times to obtain a production prompt, and prompts, as taught by Chintagunta , in order to provide a model that uses only a small amount of human labeled data to learn an effective medical dialogue summarizer and such a model to be used in a practical practitioner-in-the-loop setting where medical correctness and patient privacy are of paramount importance.). ( Chintagunta Paragraph [0136]). Anthony teaches: first prompt (Paragraphs [0005]-[0006] discuss a prompt engineering interface and generation of a first prompt.); a second prompt (Paragraphs [0005]-[0006] discuss a prompt engineering interface and generation of a second prompt.); (iv)setting, by the automated prompt manager, the second prompt as the respective first prompt (Paragraph [0065] discusses as the user continues to interact with application, application may regenerate prompt with the updated context of the new interactions and submit the updated prompt to model.). Therefore, it would have been obvious to one of ordinary skill in the art to modify Lipton to include, first prompt, a second prompt, and (iv)setting, by the automated prompt manager, the second prompt as the respective first prompt as taught by Anthony , in order to provide accurate and reliable ML models that facilitate troubleshooting. ( Anthony Paragraph [0004]). Regarding claims 2, 9, and 16 , Lipton discloses wherein the first machine-learning model is different from the second machine-learning model, and wherein the second machine-learning model is the same as the third machine-learning model (Examiner notes that the prior art does not explicitly recite first, second, or third, however, the prior art references various models may be used.) (Paragraphs [0084], [0087]-[0089], and [0104] discuss a single suite of models may be used or various models in combination, each of these pipelines still allows a set of several choices of the specific models to employ for each subtask and can execute several different models for each of the subtasks.). Regarding claims 4, 11, and 18 , Lipton discloses wherein each first label of the plurality of first labels causes a machine-learning model to perform a task associated with generating the SOAP note when provided to the machine-learning model (Paragraphs [0040], [0058], [0084], [0101]-[0107] discuss assigns the extracted noteworthy utterances to summary section(s), clusters the noteworthy utterances on a per-section basis, generates summary sentences by conditioning on the corresponding cluster and the subsection of the SOAP sentence to be generated uses a neural network or other trained machine learning model to analyze the physician-patient sessions and update the model using feedback data from the physician provided after one or more encounters, and allows a set of several choices of the specific models to employ for each subtask and can execute several different models for each of the subtasks.). Lipton does not explicitly disclose: prompts. Chintagunta teaches: prompts (Paragraph [0030] discusses dynamically construct prompts for tasks by conditioning on relevant patient information and use a generative machine learning model.). Therefore, it would have been obvious to one of ordinary skill in the art to modify Lipton to include, prompts, as taught by Chintagunta , in order to provide a model that uses only a small amount of human labeled data to learn an effective medical dialogue summarizer and such a model to be used in a practical practitioner-in-the-loop setting where medical correctness and patient privacy are of paramount importance.). ( Chintagunta Paragraph [0136]). Claim 8, discloses the same limitations as Claim 1 with the addition of: a system comprising (Paragraph [0004] discusses a system.): one or more processing systems (Paragraph [0004] discusses a data processing system.); and one or more computer-readable media storing instructions which, when executed by the one or more processing systems, cause the system to perform operations comprising (Claim 21 discusses one or more non-transitory computer-readable media storing instructions that, when executed by at least one processing device, cause the at least one processing device to perform operations.). Claim 15, discloses the same limitations as Claim 1 with the addition of one or more non-transitory computer-readable media storing instructions which, when executed by one or more processors, cause a system to perform operations comprising (Claim 21 discusses one or more non-transitory computer-readable media storing instructions that, when executed by at least one processing device, cause the at least one processing device to perform operations.) . 07-21-aia AIA Claim s 3, 5-7, 10, 12-14, 17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lipton in view of Chintagunta and Anthony and in further view of Zha (U.S. Pub. No. 2024/0202458 A1) . Regarding claims 3 , 10, and 17 , Lipton discloses wherein providing the label to the particular machine-learning model causes the particular machine-learning model to obtain a result associated with a portion of the SOAP note (Paragraphs [0038]-[0042], [0044], [0104], and FIG. 9 discuss machine learning optimized SOAP notes based on the human generated transcripts, uses the historical data to iteratively train the machine learning model, the data processing system extracts the noteworthy utterances using multi-label classification and assigns the extracted noteworthy utterances to summary section(s) and allows a set of several choices of the specific models to employ for each subtask.). Lipton does not explicitly disclose: wherein each prompt of the plurality of first prompts is a prompt for a particular machine-learning model from among a set of machine-learning models, wherein providing the prompt to the particular machine-learning model causes the particular machine-learning model to obtain a result. Zha teaches: wherein each prompt of the plurality of first prompts is a prompt for a particular machine-learning model from among a set of machine-learning models, wherein providing the prompt to the particular machine-learning model causes the particular machine-learning model to obtain a result (Paragraphs [0021]-[0023] discuss different NPL ML models may achieve different results for NLP tasks, some NLP ML models may produce better results for text summarization, as they may have been trained or developed specifically for that task, in addition to the training data and design of NLP ML models, the influence of a prompt on NLP ML model performance may vary, with variance of prompt performance used for a specific NLP task as well as variance of prompt performance across different NLP ML models.). Therefore, it would have been obvious to one of ordinary skill in the art to modify Lipton to include, wherein each prompt of the plurality of first prompts is a prompt for a particular machine-learning model from among a set of machine-learning models, wherein providing the prompt to the particular machine-learning model causes the particular machine-learning model to obtain a result, as taught by Zha , in order to provide improved accuracy, increased productivity, and cost savings to better interact with human users. ( Zha Paragraphs [0001] and [0020]). Regarding claims 5, 12, and 19 , Lipton discloses further comprising: generating a SOAP note using the collection of labels, the SOAP note documenting an encounter between a first entity and a second entity (Paragraphs [0004], [0038]-[0042], [0044], FIG. 9 discuss machine learning optimized SOAP notes based on the human generated transcripts, uses the historical data to iteratively train the machine learning model, the data processing system extracts the noteworthy utterances between a doctor and patient using multi-label classification and assigns the extracted noteworthy utterances to summary section(s) and generates useful structure in the form of additional annotations that (i) segment each note into 15 subsections (not every subsection features in every note; and (ii) identify, for each sentence in the note, a set of corresponding supporting utterances in the conversation and generating the SOAP note.); and storing the SOAP note in a database associated with at least one of the first entity and the second entity (Paragraphs [0004] and [0017] discuss generating a structured entry for a data store, the structured entry including the generated content for that particular section and sending the structured entry to the data store for storage of the structured entry.). Lipton does not explicitly disclose: accessing the collection of prompts; collection of prompts. Zha teaches: accessing the collection of prompts (Paragraphs [0024] and [0040] discuss prompt development system for NLP ML models may host, support, and collect NLP ML models, such as NLP ML model(s) and task prompts, such as prompt, which may be grouped into collections, such as task prompt collections, indexed and searched.); collection of prompts (Paragraphs [0024] and [0040] discuss prompt development system for NLP ML models may host, support, and collect NLP ML models, such as NLP ML model(s) and task prompts, such as prompt, which may be grouped into collections, such as task prompt collections.). Therefore, it would have been obvious to one of ordinary skill in the art to modify Lipton to include, accessing the collection of prompts and collection of prompts, as taught by Zha , in order to provide improved accuracy, increased productivity, and cost savings to better interact with human users. ( Zha Paragraphs [0001] and [0020]). Regarding claims 6, 13, and 20, Lipton discloses wherein generating the SOAP note using the collection of labels comprises: accessing a text transcript, the text transcript derived from an audio recording of an interaction between the first entity and the second entity (Paragraphs [0004] and [0048] discuss receive a dataset including physician-patient conversation transcripts.) ; and providing the collection of labels and the text transcript to one or more machine-learning models to (Paragraph [0038] discusses use the generated transcripts for training machine learning models to generate SOAP notes, a unique dataset of patient visit records, including of transcripts, paired SOAP notes, and annotations marking noteworthy utterances that support each summary sentence can be generated.): retrieve a set of entities, extract facts from the text transcript based at least-in part on the set of entities, and generate the SOAP note based at least-in part on the facts (Paragraphs [0038]-[0039] discuss noteworthy utterances include relevant keywords with respect to one or more of the four (S, O, A, P) sections, the data processing system automatically identifies and classifies transcript data and associated metadata in a physician-patient session based on that data's relevance to different parts of the SOAP note.). Lipton does not explicitly disclose: collection of prompts. Zha teaches: collection of prompts (Paragraphs [0024] and [0040] discuss prompt development system for NLP ML models may host, support, and collect NLP ML models, such as NLP ML model(s) and task prompts, such as prompt, which may be grouped into collections, such as task prompt collections.). Therefore, it would have been obvious to one of ordinary skill in the art to modify Lipton to include, accessing the collection of prompts and collection of prompts, as taught by Zha , in order to provide improved accuracy, increased productivity, and cost savings to better interact with human users. ( Zha Paragraphs [0001] and [0020]). Regarding claims 7 and 14, Lipton discloses wherein the first entity is a healthcare provider, wherein the second entity is a patient associated with the healthcare provider, and wherein storing the SOAP note in the database comprises storing the SOAP note in an electronic health record associated with the patient (Paragraphs [0038] and [0056] discuss physicians and patients capture audio from their sessions, which can be used to generate automated SOAP notes and historical dataset of patient visit records, including of transcripts, paired SOAP notes, and annotations marking noteworthy utterances that support each summary sentence can be stored in a database containing patient data.). Response to Arguments Applicant’s arguments filed 2/13/2026 have been fully considered. Rejections under 35 U.S.C. 101: With respect to claim 1 and the 35 U.S.C. 101 rejection, Applicant’s amendment fails to overcome the previous rejection. Claim 1 as amended recites an abstract idea, a method of organizing human activity or mathematical concepts. See MPEP 2106.04(a)(2)(II)(C) Managing Personal Behavior or Relationships or Interactions Between People and MPEP 2106.04(a)(2)(I) Mathematical Concepts. Applicant states the claims are directed to a technical improvement, “the claims are directed to a specific improvement in the functioning of computers, specifically in the field of Natural Language Processing (NLP), and automated response generation using machine learning. Amended Claim 1 is not directed to a mere method of organizing human activity, but rather to a specific technical improvement for the generation of clinical notes. As noted in the Specification at paragraph [0034], standard large language models (LLMs) are technically limited by "input window token limits" and are "error prone” hallucinate, use incorrect terminology, and overlook important facts)".”. (Remarks, pages 10-11). Examiner respectfully disagrees. The amendments fall short of resulting in an improvement or claiming the specific improvement to the way the computer operates or the machine learning model technology, and are only an improvement to the abstract idea. Examiner notes that the Application is automating human activity, confirming that what the first model generated is based on facts found in the patient/doctor notes and the automated prompt manager keeps refining to get the best prompt. However, as written, the Application is not improving the machine learning model technology or any other technology. Accordingly, the improvement is to the abstract idea. Prong Two of Step 2A While practical application is a way to overcome the Prong 2 35 U.S.C. 101 rejection, claim 1 as written fails to result in a practical application. Applicant states, “an "automated prompt manager" that programmatically refines prompts without human intervention to help ensure the accuracy of generated SOAP notes. Amended claim 1 is integrated into a practical application because it provides a specific technical solution for Automated Prompt Engineering (APE). The automated prompt manager operates autonomously and the specification recites that the "Automated Prompt Engineering (APE) service ... "automatically (e.g., without user interaction) refines a prompt using one or more iterations of providing the prompt to a first machine-learning model to generate an output, evaluating the output using a second machine-learning model, and generating a refined prompt using a third machine-learning model based on the output of the second machine-learning model.” (Remarks, page 11). Examiner respectfully disagrees. The automated prompt manager is generating a prompt to get a specific result, using rules to do what a human would do. Accordingly, the application is organizing human activity or mathematical concepts, directed to the abstract idea of providing automatic prompt engineering using a large language model. The machine learning model is part of the abstract idea. The additional elements include a system, one or more processing systems, however, they do not result in a practical application as they are recited at an apply it level, as stated above. Here, the improvement is to the abstract idea. While the claims may use three machine learning models, the claims do not improve any technology. All components in the claims are being used for their intended purpose and as written do not result in a practical application or significantly more than the abstract idea. Rejections under 35 U.S.C. 103: With respect to claim 1 and the 35 U.S.C. 103 rejection, Applicant’s amendment overcomes the previous 35 U.S.C. 103 rejection. Applicant’s arguments with respect to the amended claim 1 have been considered and the Examiner’s rejection has been updated to address Applicant’s claim amendments. Applicant states, “The Office relies on Chintagunta to teach iterative refinement of results. Chintagunta, however, relies on human involvement in the "refinement process". Paragraph [0029] of Chintagunta recites that the refinement process "requires only a few dozen annotator decisions."”. (Remarks, page 9). Examiner respectfully disagrees and notes that Chintagunta discusses, using a turn-level model that iteratively refines the sentence level pseudo-labeled dataset with the relabeled clusters and retraining the sentence level model with the refined sentence level pseudo-labeled dataset. See Paragraphs [0225]-[0230]. Examiner’s rejection related to the amended claims 1, 8, and 15 has been amended. Conclusion 07-40 AIA Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAWN TRINAH HAYNES whose telephone number is (571)270-5994. The examiner can normally be reached M-F 7:30-5:15PM. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jason Dunham can be reached on (571)272-8109. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /DAWN T. HAYNES/ Art Unit 3686 /RACHELLE L REICHERT/Primary Examiner, Art Unit 3686 Application/Control Number: 18/883,782 Page 2 Art Unit: 3686 Application/Control Number: 18/883,782 Page 3 Art Unit: 3686 Application/Control Number: 18/883,782 Page 4 Art Unit: 3686 Application/Control Number: 18/883,782 Page 5 Art Unit: 3686 Application/Control Number: 18/883,782 Page 6 Art Unit: 3686 Application/Control Number: 18/883,782 Page 7 Art Unit: 3686 Application/Control Number: 18/883,782 Page 8 Art Unit: 3686 Application/Control Number: 18/883,782 Page 9 Art Unit: 3686 Application/Control Number: 18/883,782 Page 10 Art Unit: 3686 Application/Control Number: 18/883,782 Page 11 Art Unit: 3686 Application/Control Number: 18/883,782 Page 12 Art Unit: 3686 Application/Control Number: 18/883,782 Page 13 Art Unit: 3686 Application/Control Number: 18/883,782 Page 14 Art Unit: 3686 Application/Control Number: 18/883,782 Page 15 Art Unit: 3686 Application/Control Number: 18/883,782 Page 16 Art Unit: 3686 Application/Control Number: 18/883,782 Page 17 Art Unit: 3686 Application/Control Number: 18/883,782 Page 18 Art Unit: 3686 Application/Control Number: 18/883,782 Page 19 Art Unit: 3686 Application/Control Number: 18/883,782 Page 20 Art Unit: 3686
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